字符串 ') and Issue_No=(select Issue_No from OA where Script_ID=@Script_ID) order by ID ' 后的引号不完整。 ') and Issue_No=(select Issue_No from OA where Script_ID=@Script_ID) order by ID ' 附近有语法错误。 加权CCA多信息融合的步态表征方法-《智能系统学报》

[1]吕卓纹,王一斌,邢向磊,等.加权CCA多信息融合的步态表征方法[J].智能系统学报,2019,14(03):449-454.[doi:10.11992/tis.201808012]
 LYU Zhuowen,WANG Yibin,XING Xianglei,et al.A gait representation method based on weighted CCA for multi-information fusion[J].CAAI Transactions on Intelligent Systems,2019,14(03):449-454.[doi:10.11992/tis.201808012]
点击复制

加权CCA多信息融合的步态表征方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
449-454
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
A gait representation method based on weighted CCA for multi-information fusion
作者:
吕卓纹1 王一斌1 邢向磊2 王科俊2
1. 四川师范大学 工学院, 四川 成都 610068;
2. 哈尔滨工程大学 自动化学院, 黑龙江 哈尔滨 150001
Author(s):
LYU Zhuowen1 WANG Yibin1 XING Xianglei2 WANG Kejun2
1. School of Engineering, Sichuan Normal University, Chengdu 610068, China;
2. College of Automation, Harbin Engineering University, Harbin 150001, China
关键词:
步态表征步态流图时序信息信息编码颜色空间类能量图典型相关分析单通道
Keywords:
gait representationgait flow imagetime informationinformation codingcolor spaceclass energy imagecanonical correlation analysissingle channel
分类号:
TP391.41
DOI:
10.11992/tis.201808012
摘要:
为了解决步态识别中步态表征不完备的问题,提出了一种新的步态表征方法。该方法是在步态流图的基础上,将能够表征时序信息的步宽特征编码到颜色空间,得到三通道的彩色类能量图,采用典型相关分析将多通道信息融合成单通道,同时去除了特征间的冗余信息,得到了更丰富的有益识别的步态特征。实验结果表明,提出的新方法能够有效提取步态特征,步态识别率得到显著提高。
Abstract:
In this paper, we propose a new gait representation method to address the problem of incomplete gait representation in current gait recognition technologies. The proposed method is based on gait flow images in which step-width timing information is encoded into the color space to obtain a three-channel colored class energy image. Multi-channel information can be fused into a single channel by canonical correlation analysis, whereby redundant feature information is removed. This process enriches the gait features and enables better recognition. Experimental results show that the proposed method effectively characterizes the gait features and significantly improves the recognition rate.

参考文献/References:

[1] CHEN Xin, WENG Jian, LU Wei, et al. Multi-gait recognition based on attribute discovery[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 40(7):1697-1710.
[2] GADALETA M, ROSSI M. IDNet:smartphone-based gait recognition with convolutional neural networks[J]. Pattern recognition, 2018, 74:25-37.
[3] BEN Xianye, ZHANG Peng, LAI Zhihui, et al. A general tensor representation framework for cross-view gait recognition[J]. Pattern recognition, 2019, 90:87-98.
[4] EL-ALFY H, MITSUGAMI I, YAGI Y. Gait recognition based on normal distance maps[J]. IEEE transactions on cybernetics, 2018, 48(5):1526-1539.
[5] AGGARWAL H, VISHWAKARMA D K. Covariate conscious approach for Gait recognition based upon Zernike moment invariants[J]. IEEE transactions on cognitive and developmental systems, 2018, 10(2):397-407.
[6] 何逸炜, 张军平. 步态识别的深度学习:综述[J]. 模式识别与人工智能, 2018, 31(5):442-452 HE Yiwei, ZHANG Junping. Deep learning for gait recognition:a survey[J]. Pattern recognition and artificial intelligence, 2018, 31(5):442-452
[7] HAN Ju, BHANU B. Individual recognition using gait energy image[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(2):316-322.
[8] ZHANG Erhu, ZHAO Yongwei, XIONG Wei. Active energy image plus 2DLPP for gait recognition[J]. Signal processing, 2010, 90(7):2295-2302.
[9] BASHIR K, XIANG Tao, GONG Shaogang. Gait recognition without subject cooperation[J]. Pattern recognition letters, 2010, 31(13):2052-2060.
[10] LAM T H W, CHEUNG K H, LIU J N K. Gait flow image:a silhouette-based gait representation for human identification[J]. Pattern recognition, 2011, 44(4):973-987.
[11] LEE C P, TAN A W C, TAN S C. Gait probability image:an information-theoretic model of gait representation[J]. Journal of visual communication and image representation, 2014, 25(6):1489-1492.
[12] DENG Muqing, WANG Cong, ZHENG Tongjia. Individual identification using a gait dynamics graph[J]. Pattern recognition, 2018, 83:287-298.
[13] 陈实, 马天骏, 黄万红, 等. 用于步态识别的多层窗口图像矩[J]. 电子与信息学报, 2009, 31(1):116-119 CHEN Shi, MA Tianjun, HUANG Wanhong, et al. A multi-layer windows method of moments for gait recognition[J]. Journal of electronics and information technology, 2009, 31(1):116-119
[14] HOFMANN M, GEIGER J, BACHMANN S, et al. The TUM gait from audio, image and depth (GAID) database:Multimodal recognition of subjects and traits[J]. Journal of visual communication and image representation, 2014, 25(1):195-206.
[15] XING Xianglei, WANG Kejun, YAN Tao, et al. Complete canonical correlation analysis with application to multi-view gait recognition[J]. Pattern recognition, 2016, 50:107-117.
[16] GAO Lei, QI Lin, CHEN Enqing, et al. Discriminative multiple canonical correlation analysis for information fusion[J]. IEEE transactions on image processing, 2018, 27(4):1951-1965.
[17] 石强, 张斌, 陈喆, 等. 异质影像融合研究现状及趋势[J]. 自动化学报, 2014, 40(3):385-396 SHI Qiang, ZHANG Bin, CHEN Zhe, et al. Fusion techniques for heterogeneous images:a survey[J]. Acta automatica sinica, 2014, 40(3):385-396
[18] WANG Chen, ZHANG Junping, WANG Liang, et al. Human identification using temporal information preserving gait template[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(11):2164-2176.
[19] SUN Shiliang. A survey of multi-view machine learning[J]. Neural computing and applications, 2013, 23(7/8):2031-2038.
[20] SARKAR S, PHILLIPS P J, LIU Z, et al. The humanID gait challenge problem:data sets, performance, and analysis[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(2):162-177.

备注/Memo

备注/Memo:
收稿日期:2018-08-15。
基金项目:四川省教育厅科研基金项目(18ZB0488)
作者简介:吕卓纹,女,1988年生,讲师,博士,主要研究方向为模式识别、生物特征识别。发表学术论文10余篇,其中被SCI、EI检索5篇;王一斌,男,1982年生,讲师,博士,主要研究方向为模式识别和机器视觉。发表学术论文10余篇,其中被SCI,EI检索7篇;邢向磊,男,1983年生,讲师,博士,主要研究方向为机器学习、模式识别。发表学术论文30余篇,其中被SCI、EI检索10余篇。
通讯作者:吕卓纹.E-mail:695946953@qq.com
更新日期/Last Update: 1900-01-01